Physics-based Forecasting
Physics-Based Forecasting is a forecasting methodology that belongs to the class of model-driven approaches in reservoir forecasting, distinguished by its reliance on fundamental physical laws and mathematical representations of subsurface processes to predict reservoir behavior.
127
Publications
6.2K
Citations
551
Authors
234
Institutions
Publications per year
2017–2026
104
Parents
127
Probabilistic weather forecasting with machine learning
Ilan Price, Álvaro Sánchez‐González, Ferran Alet et al. · Nature · 2024 · 306 citations · Full text
Deep learning for twelve hour precipitation forecasts
Lasse Espeholt, Shreya Agrawal, Casper Kaae Sønderby et al. · Nature Communications · 2022 · 238 citations · Full text
1–5 of 127
551
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Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland
2 publications · 251 citations
2
h-index
1–5 of 551
234
Massachusetts Institute of Technology
Cambridge, United States
5 h-index
13 publications
1.4K citations
Stanford University
Stanford, United States
5 h-index
9 publications
1.1K citations

Pacific Northwest National Laboratory
Richland, United States
5 h-index
8 publications
290 citations
Loughborough University
Loughborough, United Kingdom
4 h-index
4 publications
429 citations
Google (United States)
Mountain View, United States
4 h-index
19 publications
3.5K citations

NSF National Center for Atmospheric Research
Boulder, United States
4 h-index
4 publications
285 citations
University of Washington
Seattle, United States
4 h-index
5 publications
96 citations
United States Geological Survey
Reston, United States
3 h-index
10 publications
288 citations
1–8 of 234
Repository · 9 publications · 228 citations
9
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4 publications · 181 citations
4
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3 publications · 487 citations
3
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SRL · 3 publications · 111 citations
3
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3 publications · 150 citations
3
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